LEADER 03712nam 22005535 450 001 9911047829003321 005 20251029120413.0 010 $a9783032089700$b(electronic bk.) 010 $z9783032089694 024 7 $a10.1007/978-3-032-08970-0 035 $a(MiAaPQ)EBC32383137 035 $a(Au-PeEL)EBL32383137 035 $a(CKB)41986548700041 035 $a(DE-He213)978-3-032-08970-0 035 $a(OCoLC)1549517318 035 $a(EXLCZ)9941986548700041 100 $a20251029d2026 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHuman-AI Collaboration $eFirst International Workshop, HAIC 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 27, 2025, Proceedings /$fedited by Xiaoqing Guo, Yueming Jin, Hala Lamdouar, Qianhui Men, Cheng Ouyang, Manish Sahu, S. Swaroop Vedula 205 $a1st ed. 2026. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2026. 215 $a1 online resource (151 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v16214 311 08$aPrint version: Guo, Xiaoqing Human-AI Collaboration Cham : Springer,c2025 9783032089694 327 $a -- Design and assessment for joint systems and workflows. -- Beyond Manual Annotation: A Human-AI Collaborative Framework for Medical Image Segmentation Using Only ?Better or Worse? Expert Feedback. -- A methodology for clinically driven interactive segmentation evaluation. -- Interactive environments for clinical training, education,and human-AI teaming. -- Explainable AI for Automated User-specific Feedback in Surgical Skill Acquisition. -- Real-Time, Dynamic, and Highly Generalizable Ultrasound Image Simulation-Guided Procedure Training System for Musculoskeletal Minimally Invasive Treatment. -- Human-in-the-loop model training. -- Learning What is Worth Learning: Active and Sequential Domain Adaptation for Multi-modal Gross Tumor Volume Segmentation. -- Guided Active Learning for Medical Image Segmentation. -- Applications of human-AI interaction, collaboration, and human factor analysis. -- User Perception of Attention Visualizations: Effects on Interpretability Across Evidence-Based Medical Documents. -- Simulating Inter-observer Variability Across Clinical Experience Levels. -- Boosting transparency, interpretability, and risk management. -- Perceptual Evaluation of GANs and Diffusion Models for Generating X-rays. 330 $aThis book constitutes the refereed proceedings of the First International Workshop, HAIC 2025, held in Conjunction with MICCAI 2025, Daejeon, South Korea, in September 27, 2025. The 9 full papers presented in this book were carefully selected and reviewed from 12 submissions. These papers have been organized in the following topical sections: Medical image computing; computer-assisted intervention; human-ai collaboration; human-computer interaction; human factor modeling; medical image analysis. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v16214 606 $aComputer science 606 $aComputer Science 615 0$aComputer science. 615 14$aComputer Science. 676 $a004 700 $aGuo$b Xiaoqing$01860779 701 $aJin$b Yueming$01431761 701 $aLamdouar$b Hala$01860780 701 $aMen$b Qianhui$01860781 701 $aOuyang$b Cheng$01785285 701 $aSahu$b Manish$01860782 701 $aVedula$b S. Swaroop$01860783 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9911047829003321 996 $aHuman-AI Collaboration$94466506 997 $aUNINA